Natural language interpretation of hierarchical data

ABSTRACT

A computer-implemented method includes receiving a search label and accessing a hierarchical data source comprising a plurality of nodes. One node may be a context node. The method further includes determining a similarity score between the search label and a node label of each node, determining a contextual score between the context node and each node, combining, for each node, the similarity score with the contextual score to yield a combined score, and returning a result. The result may be based on ordering the plurality of nodes according to each node&#39;s combined score. A corresponding computer program product and computer system are also disclosed.

BACKGROUND

The present invention relates generally to natural language queries andin particular to scoring the results of querying hierarchical data basedon a natural language label.

Multidimensional hierarchical data sets such as OnLine AnalyticalProcessing (OLAP) cubes have become widely used and increasinglydetailed. Simultaneously, interactive natural language search systemssuch as Apple® Siri®, Google® Now™, and Microsoft® Cortana™ have becomeincreasingly sophisticated and useful for querying a variety ofdifferent data sources. The invention relates to the application ofinteractive natural language search systems to querying multidimensionalhierarchical data sets.

SUMMARY

A computer-implemented method includes receiving a search label andaccessing a hierarchical data source comprising a plurality of nodes.One node may be a context node. The method further includes determininga similarity score between the search label and a node label of eachnode, determining a contextual score between the context node and eachnode, combining, for each node, the similarity score with the contextualscore to yield a combined score, and returning a result. The result maybe based on ordering the plurality of nodes according to each node'scombined score. A corresponding computer program product and computersystem are also disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram of one embodiment of a computer systemenvironment suitable for operation of the invention.

FIG. 1B is a diagram of various logical components of the hierarchicaldata source 103, according one embodiment of the invention.

FIG. 2 is a flow chart diagram of a method for the hierarchical queryprogram 101, according to one embodiment of the invention.

FIG. 3 is a diagram of an example data set according to one embodimentof the invention.

FIG. 4 is a table showing one possible representation of the result list104 of an example data set according one embodiment of the invention.

FIG. 5 is a block diagram of one example of a computing apparatus 100suitable for executing the hierarchical query program 101.

DETAILED DESCRIPTION

Referring now to the invention in more detail, the invention isgenerally directed to improvements in scoring the results of queryinghierarchical data based on a natural language label. FIG. 1A is a blockdiagram of a computer system environment in which one embodiment of theinvention may operate. FIG. 1A displays the computing apparatus 100, thehierarchical query program 101, the search label 102, the hierarchicaldata source 103, and the result list 104. In particular, a hierarchicalquery program 101 may be configured for execution by, operation on,and/or storage on and/or in a computing apparatus 100. The hierarchicalquery program 101 receives a search label 102, accesses a hierarchicaldata source 103, and returns, generically, a result. In one embodiment,the result is a result list 104, as shown.

Referring still to FIG. 1A, the search label 102 includes data to bequeried against a hierarchical data source 103. The search label may bea text string, but may also be an image, sound clip, video clip, or aportion thereof, or generally any type of data.

FIG. 1B shows a diagram of the various logical components of thehierarchical data source 103, according to one embodiment of theinvention. FIG. 1B displays the hierarchical data source 103, the nodes103A, the context node 105, the node labels 107, the similarity scores108, the contextual scores 109, and the combined scores 110. Within thedepicted hierarchical data source 103, there is a plurality of nodes103A, each having a node label 107. It may be advantageous that the nodelabels 107 and the search label 102 are of the same or a similar type ofdata such that comparison between the search label 102 and the nodelabels 107 is feasible and meaningful.

In one embodiment, the node labels 107 and the search label 102 are textstrings. The node labels 107 and search label 102 may be naturallanguage text according to standard spelling or to phonetic spelling.Standard spelling of natural language text, for example according tovarious dictionaries in English or another human language, may be used,for example, for querying where a query is entered as text by the user.Where the query is entered by automated speech recognition (ASR), as inan interactive natural language search system, the search label 102 andthe node labels 107 may be stored phonetically spelled natural languagetext. The phonetic spelling may be according to the InternationalPhonetic Alphabet or to an alternative phonetic spelling system forEnglish or another human language.

Referring still to FIGS. 1A and 1B, in one embodiment, each node mayhave associated therewith a similarity score 108. The hierarchical queryprogram 101 may determine the similarity score 108 based on the resultsof a comparison between the search label 102 and each node label 107.Where the search label 102 and the node labels 107 represent image,sound, or video data, or data of some other type, the hierarchical queryprogram 101 may make the comparison according to any method or systemthat returns a numerical score representing the similarity between thesearch label 102 and each node label 107. In one embodiment, thehierarchical query program 101 may normalize the similarity scores 108into positive integers or floating point numbers, thereby preventingscores of zero, where the lesser the number (closer to zero) representsa more similar result.

In the case of ASR systems, a confidence score may be provided as to thematch between a speech utterance and one or more strings. Thehierarchical query program 101 may be integrated with this process, forexample, the hierarchical query program may use the system's confidencescore algorithm to directly compare the search label 102 and each nodelabel 107. Alternatively, the ASR system may operate as a separatelayer, returning one or more textual search labels 102 for thehierarchical query program 101 to test. In the case where an array ofcandidate search labels 102 is provided, for example as the output of anASR system, then the hierarchical query program 101 need only repeat foreach candidate search label 102, and the hierarchical query program mayreturn combined or separate results.

In the case where the search label 102 and the node labels 107 are bothtext strings representing natural language text, whether according tostandard spelling or phonetic spelling, the hierarchical query program101 may determine a comparison score, understood as the similarity score108, by calculating the edit distance, or Levenshtein distance (as usedherein, and as claimed, the terms “edit distance” and “Levenshteindistance” are interchangeable) between the search label 102 and the nodelabels 107. In the basic Levenshtein distance algorithm, the result isthe total count of insertions of a single character, deletions of asingle character, or substitutions of one character for another that areneeded to convert a first string into a second string. In the resultingscores, lesser numbers represent more similar results, and a score ofzero (if allowed) represents a perfect match. The Levenshtein distancealgorithm may be modified in numerous ways, for example where thevarious operations—insertions, deletions, and substitutions—are givendiffering weights, or where different operations are allowed, forexample where substitutions are not treated as a single operation, orwhere the transposition of two adjacent characters is allowed as asingle operation. Generally, any such modification to the Levenshteindistance or any relevant string metric may be used equivalently in thecontext of the present invention, subject to a restriction, for someembodiments, that the results be positive nonzero values with lessernumbers representing a more similar result.

Referring still to FIGS. 1A and 1B, according to one embodiment, thehierarchical query program accesses the hierarchical data source 103 asa tree-type graph of nodes 103A. In a tree-type graph, each node has aparent (except for the root node, LO in FIG. 1) and zero or more childnodes. For example, in FIG. 1, L2 is a child of L0 and has children L5and L6. Any desired constraints may be imposed upon the structure of thehierarchical data source 103, for example a requirement of a binary tree(a tree where each node has either two children or no children) or arequirement to have exactly n children may be imposed upon thehierarchical data source 103. In some embodiments, multiple parentalrelationships and cycles of parent-child relationships may be allowed.

In OLAP cubes and other hierarchical databases, nodes may be constrainedin terms of their data content or label as well as their position in thehierarchy. A specific constraint contemplated in one embodiment of thepresent invention is that the hierarchical data source 103 is structuredsuch that each child node represents, conceptually, a specific instanceof its parent. A specific data point may be represented as a locus ofnodes in any of several hierarchies, each hierarchy representing adimension of the overall data (e.g. an OLAP cube), and modelable in amanner similar to the example hierarchical data source 103 of FIG. 1B.For example, a quantity representing sales of golf equipment in Torontoin 2014, may be accessed in the time dimension via a node 103A having anode label 107 “2014”, and which is the child of a node 103A whose nodelabel 107 is “2010-2019”, representing the current decade.

This node 103A would, itself, be a child of a node 103A whose node label107 is “time”. Similarly, the “2014” node 103A may have child nodesrepresenting smaller elements, such as sibling nodes 103A whose nodelabels 107 are “Q1”, “Q2”, “Q3”, and “Q4”, denoting the four quarters ofthe year 2014. The same general-to-specific hierarchy may be repeatedfor each dimension of the data set. Accordingly, a the hierarchicalquery program may consider each dimension of the data set as a separatehierarchy or may model multiple dimensions as different branches of thesame hierarchy.

In one embodiment, there is a given context node 105. The context node105 may be generally understood as the node 103A most recently viewed,accessed, or considered. In the above example of a query for sales ofgolf equipment in Toronto in 2014, the query could, in a session of aninteractive natural language search system, be succeeded by a query “Howabout 2013?”. In this case, “2014” would be the node label 107 of thecontext node 105. The context node 105 may be within the hierarchicaldata source 103 that represents a time dimension, and from which thehierarchical query program 101 may perform a context-aware search.Similarly, “Toronto” represents the context in a geographic dimension,“sales” represents the context in a type-of-quantity dimension, and“golf equipment” represents the context in a products dimension.

Referring still to FIGS. 1A and 1B, the result list 104 represents onepossible output of the hierarchical query program 101. In the embodimentdescribed below, all nodes 103A the hierarchical query program 101iterates over all nodes 103A of the hierarchical data source 103. Thehierarchical query program 101 may traverse the hierarchical data source103 along parent-child relationships, or independently of the hierarchy.In the depicted embodiment, the hierarchical query program 101 does notmodify the hierarchical relationships among the nodes 103A. Thehierarchical query program 101 may store the results in-situ, asproperties of nodes 103A, or externally to the hierarchical data source,for example in the result list 104.

It should be noted that the visual representation of the similarityscores 108 in FIG. 1B as associated to the nodes 103A does require thatthe similarity scores 108 be stored in-situ as properties of nodes 103A.In general, the result list 104 may be any data structure. For example,the result list 104 may be a list, array, or other linear data structureincluding references to nodes 103A in relationship to the variouscalculated values described below. In other embodiments, the resultslist may be a reference to the single best node 103A, or a list ofreferences to only the best n nodes 103A for a given n, where n isgreater than or equal to one. The discussion herein of one embodimentuses, for the purpose of clarity, a rich output including calculationresults, such as the example result list 104 of FIG. 4, however thislevel of detail is not a requirement of the present invention.

Referring now to FIG. 2, FIG. 2 shows a flow chart diagram describingthe operation of the hierarchical query program 101, according to oneembodiment. FIG. 2 displays steps 210, 211, 212, 213, 214, 215, 216,217, and 218. At step 210, the hierarchical query program receives thesearch label 102. As above, there may be multiple search labels 102, forexample where an ASR system returns an array of candidate search labels102, or where multiple words or phrases are presented as part of thesame query, as in a request for “sales of golf equipment in Toronto on2014”. In these environments, the hierarchical query program 101 mayrepeat as to all search labels 102. At step 211, the hierarchical queryprogram 101 accesses the hierarchical data source 103.

At step 212, the hierarchical query program 101 iterates over all nodes103A. Formally, steps 212 describes the iteration as proceeding to thenext node 103A until no nodes 103A remain. The hierarchical queryprogram 101 may iterate by any means, with the various steps movedinside or outside of the displayed loop, as needed, according toordinary computer programming techniques. Neither the start and endnodes 103A nor the order in which the nodes 103A are examined is ofsignificance in one embodiment of the invention. However, it may becomputationally advantageous to iterate over the nodes by traversing thehierarchical data source 103 through its parent-child relationships. Inparticular, this may be computationally advantageous when thehierarchical query program 101 calculates the contextual score 109 asdescribed at step 214.

At step 213, the hierarchical query program determines the similarityscore 108 for the node. As above, this may be done by any comparisonalgorithm. In one embodiment, text strings may be the content of thesearch label 102 and the node labels 107, and thus the hierarchicalquery program 101 takes the similarity score 108 as the edit distance orLevenshtein distance between the search label 102 and the node label107, plus 1. It is may be useful, in one embodiment, to add one to theLevenshtein distance, strictly because of an artefact of the exemplarycombination function for step 215—namely, multiplication with thecontextual score 109. By adding 1 to the Levenshtein distance, thehierarchical query program 101 ensures that the similarity score 108will have a minimum value of 1, and the information content of thecontextual score 109 will not be eliminated as a result ofmultiplication by zero in the case of a perfect string match. Thisallows the hierarchical query program 101 to compare multiple perfectstring matches on their positions in the hierarchical data source 103,relative to the context node 105.

Referring still to FIG. 2, at step 214, the hierarchical query program101 compares the node 103A with the context node 105 to yield acontextual score 109. Abstractly, the contextual score may be generatedby any means for encapsulating the distance, within the hierarchicaldata source 103, from the context node 105 to the node 103A, or thereverse. In one embodiment, the unmodified graph distance is used. Thehierarchical query program reaches this value by counting theparent-child relationships within the hierarchical data source 103 thatmust be traversed in a path from the context node 105 to node 103A orvice versa. The graph distance may be modified in any number of ways;for example, traversing downward (i.e. more specific) from the contextnode 105 may be weighted more favorably (numerically lesser) thantraversing up from the context node 105; the result of this is to tendto favor results that represent a more specific instance of the contextnode 105 over those that are more general.

At step 215, the hierarchical query program 101 combines contextualscore 109 and the similarity score 108 for the node 103A to yield acombined score 110. In one embodiment, the scores 108 and 109 arecombined by multiplying the contextual score 109 and the similarityscore 108 together. More generally, the hierarchical query program 101may employ any means of compositing the two values 108 and 109. Forexample, the scores 108 and 109 may be added, or their arithmetic meanmay be taken. Wherever the scores 108 and 109 are to be multiplied, itmay be advantageous for the values to be nonzero. For the similarityscore 108, one embodiment of the hierarchical query program 101 takesthe Levenshtein distance plus one, thus ensuring a minimum score of 1 inthe case of a perfect match. For the contextual score 109, wherevergraph distance is taken, a graph distance of zero will occur only forthe context node 105. For this reason, the hierarchical query program101 discards the context node 105, or any nodes 103A with contextualscore 109, at step 216, below.

Referring still to FIG. 2, at step 216, the hierarchical query program101 sanitizes the results. Specifically, the hierarchical query programdiscards some of the nodes 103A. Any discarded nodes may be removed ordropped from the result list 104, regardless of how the result list 104is structured. More generally, step 216 may be understood as thehierarchical query program 101 discarding one or more of the nodes 103Aaccording to one or more discard criteria. In general, the hierarchicalquery program 101 may apply, as discard criteria, any heuristic thattends to identify nodes 103A that are unlikely to be valuable results.

In one embodiment, the hierarchical query program 101 applies twoheuristics as discard criteria. The first of these, applicable where thesearch label 102 and the node labels 107 are text strings, is that anynodes 103A for which the Levenshtein distance of the node label 107 tothe search label 102 exceeds the length of the node label 107 should bediscarded. In one embodiment, the hierarchical query program takes suchresults as complete misses, and thus discards them. In one embodiment,the hierarchical query program 101 treats the similarity score 108 asthe Levenshtein distance plus 1 for the purpose of avoiding a similarityscore 108 of zero. Thus, in the example of FIGS. 3-4, and in anyembodiment where the similarity score 108 is taken for the Levenshteindistance plus one, the hierarchical query program compares thesimilarity score 108 to the length, plus one, of the node label 107. Ingeneral, and as claimed, the relevant heuristic is that, where the editdistance, however computed and disregarding any adjustments such asadding one, between the search label 102 and the node label 107 isgreater than or equal to the length of the node label 107, the node 103Ashould be discarded as a viable result.

The second discard criterion is that the hierarchical query programshould discard the context node 105, identified as the single node witha contextual score 109 of zero. This is applicable wherever thecontextual score 109 is determined by graph distance or modified graphdistance. This criterion represents an application of the heuristic thatthe hierarchical query program 101 should discard the same node 103A aspreviously accessed because the query source (i.e. a user) wants tochange the current context. However, it should be noted that the userlikely wants to change the current context only in one dimension, or ina limited number of dimensions of a multidimensional data structure ofwhich the hierarchical data source 103 may represent the structure ofonly one dimension.

For this reason, the heuristic does not assume that the context of anyparticular dimension will be changed, but rather, the hierarchical queryprogram returns, in the result list 104, the best matches for thecurrent dimension (i.e. the current hierarchical data source 103), withthe understanding that the true user intent may not be to change thecontext in the current dimension at all. In the example where theprevious query requests “sales of golf equipment in Toronto in 2014”followed by “how about 2013?”, the user plainly wants to change thecontext only in the time dimension from 2014 to 2013, however computingapparatus 100 may not be directly aware of this, and thus thehierarchical query program 101 operate on more than one of the availabledimensions.

Referring still to FIG. 2, at step 217, the hierarchical query program101 orders the result by the combined score 110. In one embodiment, thecombined scores 110 are normalized so that lesser values represent abetter match. Where the result list 104 comprises a linear datastructure, step 217 may be understood as sorting the list, array, orother linear structure by the combined score 110. Where the hierarchicalquery program 101 retains only a single best node, it may generate theresult list 104 as a single reference, to be replaced as needed. Inother embodiments, the result list 104 comprises a linear list of allremaining (not sanitized) nodes 103A, or the best n nodes for a given n,where n is greater than or equal to one.

At step 218, the hierarchical query program 101 returns a result. Theresult may generally be of any form and generally based on the orderingof the nodes 103A by combined score 110. To return the result, thehierarchical query program 101 may transmit the result list 104 toanother layer, processor or program, may store the result list 104 inone or more computer readable storage media, may alter the hierarchicaldata source 103 in situ, or generally communicate the result.

Referring now to FIGS. 3-4, FIG. 3 shows an exemplary hierarchical datasource 103 as one embodiment of the hierarchical query program 101 mayaccess it. FIG. 3 displays the search label 102, the hierarchical datasource 103, the nodes 103A, the node labels 107, and the context node105. FIG. 4 shows the result list 104 that may follow from the exampledata of FIG. 3. FIG. 4 displays the result list 104, the node labels107, the similarity scores 108, the contextual scores 109, and thecombined scores 110. In the example of FIG. 3, the hierarchical queryprogram 101 receives the search label 102, “Ottawa”, and the givencontext node 105 has node label 107 “Toronto”. As shown, the data of theexample of FIGS. 3-4 are arranged such that each node 103A is a specificinstance of its parent, with community names being children of regionalnames, which in turn are children of national names, which in turn arechildren of the root node “places”. The greater context for the exampleshown is an exchange wherein a user has previously queried “sales ofgolf equipment in Toronto in 2014” and has now continued with “how aboutOttawa?”

FIG. 4 summarizes the results of the hierarchical query program 101 tothe data of FIG. 3, as the results might appear in a result list 104.Within the example results, the context node 105 with node label 107“Toronto” is discarded according to step 216 because it is the contextnode with contextual score 109 equal to zero. Further, the nodes withnode labels 107 “Ohio” and “Places” are discarded because theirsimilarity scores 108 are equal to or greater than one plus the lengthof their respective node labels 107.

The clear best result, in the example shown, is Ottawa, Ontario, whichhas a combined score of 2. Ottawa, Ontario is both a perfect stringmatch for the search label “Ottawa”, but also a strong contextual matchbecause it is a peer in the hierarchical data source 103—Ottawa andToronto are both child nodes of Ontario. By contrast, an equally perfectstring match for Ottawa, Ohio receives a combined score 110 of 6 due tobeing relatively disparate from the context node 105, Toronto, and thushas a less favorable contextual score 109. Similarly named nodes thatare hierarchically near Toronto, such as Mattawa, Ontario, and Ontarioitself, both receive favorable combined scores. More generally, thetable of FIG. 4 shows numerical combined scores 110 that are intuitivelysensible, given the input queries, and which perform better than couldbe done when considering either Levenshtein distance or graph distancealone.

The functionality disclosed herein may be built into an ASR naturallanguage search interface for portable electronic communication devices,for example Apple® Siri®, Google® Now™, and Microsoft® Cortana™, whichmay optionally store the hierarchical data source 103 locally or accessit over a network. Separately, the functionality disclosed herein may bebuilt into a user query interface to a multidimensional hierarchicaldata set such as an OLAP cube. The functionality disclosed herein may beincorporated into a combined system that uses an ASR interactive naturallanguage search interface to a multidimensional hierarchical data setsuch as an OLAP cube.

FIG. 5 is a block diagram depicting components of a computer 500suitable for executing the hierarchical query program 101. FIG. 5displays the computer 500, the one or more processor(s) 501 (includingcomputer processors), the communications fabric 502, the memory 503, theRAM 504, the cache 505, the persistent storage 506, the communicationsunit 507, the I/O interfaces 508, the display 509, the audio input 510,and the external devices 511. It should be appreciated that FIG. 5provides only an illustration of one embodiment and does not imply anylimitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

As depicted, the computer 500 operates over a communications fabric 502,which provides communications between the computer processor(s) 501,memory 503, persistent storage 506, communications unit 507, andinput/output (I/O) interface(s) 508. The communications fabric 502 maybe implemented with any architecture suitable for passing data orcontrol information between the processors 501 (e.g. microprocessors,communications processors, and network processors), the memory 503, theexternal devices 511, and any other hardware components within a system.For example, the communications fabric 502 may be implemented with oneor more buses.

The memory 503 and persistent storage 506 are computer readable storagemedia. In the depicted embodiment, the memory 503 comprises a randomaccess memory (RAM) 504 and a cache 505. In general, the memory 503 maycomprise any suitable volatile or non-volatile one or more computerreadable storage media.

Program instructions for the hierarchical query program 101 may bestored in the persistent storage 506, or more generally, any computerreadable storage media, for execution by one or more of the respectivecomputer processors 501 via one or more memories of the memory 503. Thepersistent storage 506 may be a magnetic hard disk drive, a solid statedisk drive, a semiconductor storage device, read-only memory (ROM),electronically erasable programmable read-only memory (EEPROM), flashmemory, or any other computer readable storage media that is capable ofstoring program instructions or digital information.

The media used by the persistent storage 506 may also be removable. Forexample, a removable hard drive may be used for persistent storage 506.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of the persistentstorage 506.

The communications unit 507, in these examples, provides forcommunications with other data processing systems or devices. In theseexamples, the communications unit 507 may comprise one or more networkinterface cards. The communications unit 507 may provide communicationsthrough the use of either or both physical and wireless communicationslinks. In the context of some embodiments of the present invention, thesource of the search label 102 may be physically remote to the computer500 such that the search label 102 may be received and the result list104 similarly transmitted via the communications unit 507.

The I/O interface(s) 508 allow for input and output of data with otherdevices that may operate in conjunction with the computer 500. Forexample, the I/O interface 508 may provide a connection to the externaldevices 511, which may be as a keyboard, keypad, a touch screen, orother suitable input devices. External devices 511 may also includeportable computer readable storage media, for example thumb drives,portable optical or magnetic disks, and memory cards. Software and dataused to practice embodiments of the present invention may be stored onsuch portable computer readable storage media and may be loaded onto thepersistent storage 506 via the I/O interface(s) 508. The I/Ointerface(s) 508 may similarly connect to a display 509. The display 509provides a mechanism to display data to a user and may be, for example,a computer monitor. The I/O interface(s) 508 may similarly connect to anaudio input 510. The audio input 510 provides a mechanism to receivespeech data from a user, as may be taken in some embodiments of thepresent invention, and may be, for example, a microphone.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a readable storage medium that can direct acomputer, a programmable data processing apparatus, and/or other devicesto function in a particular manner, such that the computer readablestorage medium having instructions stored therein comprises an articleof manufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof computer program instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:receiving a search label; accessing a hierarchical data sourcecomprising a plurality of nodes, one of said plurality of nodes being acontext node; determining a similarity score between said search labeland a node label of each of said plurality of nodes; determining acontextual score between said context node and each of said plurality ofnodes; combining, for each of said plurality of nodes, said similarityscore with said contextual score to yield a combined score; andreturning a result based on ordering said plurality of nodes accordingto said combined score of each of said plurality of nodes.
 2. Thecomputer-implemented method of claim 1, wherein said search label andsaid node label are text strings, and wherein determining a similarityscore between said search label and a node label of each of saidplurality of nodes comprises calculating an edit distance between saidsearch label and said node label of each of said plurality of nodes. 3.The computer-implemented method of claim 1, wherein determining acontextual score between said context node and each of said plurality ofnodes comprises calculating a graph distance between each of saidplurality of nodes and said context node.
 4. The computer-implementedmethod of claim 2, wherein determining a contextual score between saidcontext node and each of said plurality of nodes comprises calculating agraph distance between each of said plurality of nodes and said contextnode.
 5. The computer-implemented method of claim 4 further comprisingdiscarding one or more of said plurality of nodes according to one ormore discard criteria.
 6. The computer-implemented method of claim 5,wherein one of said one or more discard criteria comprises discardingany of said plurality of nodes for which said edit distance is greaterthan or equal to the length of said node label.
 7. Thecomputer-implemented method of claim 5, wherein one of said one or morediscard criteria comprises discarding any of said plurality of nodes forwhich said graph distance is zero.
 8. The computer-implemented method ofclaim 2, wherein said node label and said search label comprisephonetically spelled natural language.
 9. The computer-implementedmethod of claim 1, wherein said hierarchical data source is structuredsuch that each child node within said plurality of nodes represents aspecific instance of its parent.